DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM

This paper introduces DeltaKWS, to the best of our knowledge, the first ΔRNN-enabled fine-grained temporal sparsity-aware Keyword Spotting (KWS) integrated circuit (IC) for voice-controlled devices. The 65nm prototype chip features a number of techniques to enhance performance, area, and power efficiencies, specifically: 1) a bio-inspired delta-gated recurrent neural network (ΔRNN) classifier leveraging temporal similarities between neighboring feature vectors extracted from input frames and network hidden states, eliminating unnecessary operations and memory accesses; 2) an infinite impulse response (IIR) bandpass filter (BPF)-based feature extractor (FEx) that leverages mixed-precision quantization, low-cost computing structure and channel selection; 3) a 24 kB 0.6V near-VTH weight static random-access memory (SRAM) that achieves 6.6× lower read power than the foundry-provided SRAM. From chip measurement results, we show that the DeltaKWS achieves an 11/12-class Google Speech Command Dataset (GSCD) accuracy of 90.5%/89.5% respectively and energy consumption of 36 nJ/decision in 65nm CMOS process. At 87% temporal sparsity, computing latency and energy/inference are reduced by 2.4×/3.4×, respectively. The IIR BPF-based FEx, ΔRNN accelerator, and 24 kB near-VTH SRAM blocks occupy 0.084mm$^{2}$, 0.319mm$^{2}$, and 0.381mm$^{2}$ respectively (0.78mm$^{2}$ in total).